Class prototype construction and matching are core aspects of few-shot action recognition.
Previous methods mainly focus on designing spatiotemporal relation modeling modules or …

This paper studies referring video object segmentation (RVOS) by boosting video-level
visual-linguistic alignment. Recent approaches model the RVOS task as a sequence …

Few-shot action recognition aims to address the high cost and impracticality of manually
labeling complex and variable video data in action recognition. It requires accurately …

Recently, few-shot action recognition has progressed significantly, as it has learned the
feature discriminability and designed suitable comparison methods. Still, there are the …

Recent work on action recognition leverages 3D features and textual information to achieve
state-of-the-art performance. However, most of the current few-shot action recognition …

We propose a simple yet effective approach for few-shot action recognition, emphasizing the
disentanglement of motion and appearance representations. By harnessing recent progress …

In recent years, the field of few-shot action recognition (FSAR) has garnered significant
attention. Although many methods primarily rely on mono-modal data, there is a growing …

This paper focuses on the Referring Image Segmentation (RIS) task, which aims to segment
objects from an image based on a given language description. The critical problem of RIS is …

Depression is a common mental health issue that requires prompt diagnosis and treatment.
Despite the promise of social media data for depression detection, the opacity of employed …

The goal of few-shot relation extraction is to predict relations between name entities in a
sentence when only a few labeled instances are available for training. Existing few-shot …